Data Transcription and Major Topics in Corpus Linguistics

The aim of this book and its accompanying audio files is to make accessible a corpus of 40 authentic job interviews conducted in English. The recordings and transcriptions of the interviews published here may be used by students, teachers and researchers alike for linguistic analyses of spoken discourse and as authentic material for language learning in the classroom. The book includes an introduction to corpus linguistics, offering insight into different kinds of corpora and discussing their main characteristics. Furthermore, major features of the discourse genre job interview are outlined and detailed information is given concerning the job interview corpus published in this book.

2. Corpus Linguistics

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2.1 What is corpus linguistics? Setting the scene

Corpus linguistics, in the sense of searching for words and indexing them in a text, has its roots in the 13th century when scholars applied these processes to the Christian Bible (cf. McCarthy/O’Keeffe 2010: 3). According to McCarthy/ O’Keeffe (2010: 4), the American structuralists in the 1950s were “forerunners” of corpus linguists “in the sense of data gathering (…) [and] in terms of the commitment to putting real language data at the core of what linguists study”. However, the beginnings of corpus linguistics in this sense can be traced back even further, at least to the beginning of the 20th century and the work of Leonard Bloomfield (cf. Bloomfield 1917, Emons 1997: 63). The impact of corpus linguistics since then has been “enormous, transforming both how we understand and how we study language across a range of different areas” (Hyland/Huat/Handford 2012: 3). Corpora added

an empirical dimension to language studies, which was not possible before, or not possible to the same extent, allowing researchers to replace intuitions, strengthen interpretations, reinforce claims and generally talk about language with greater confidence. While corpora themselves do not provide new information about language, the ability to draw on large samples of naturally occurring data allows analysts to offer enhanced descriptions of language and how it works (…). The pace of this change has also been considerable, accelerated by the growing accessibility and processing capabilities of computers (…). (Hyland/Huat/Handford 2012: 3)

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